Generative AI chatbots as sources of web traffic: web analytics insights from a journal portal
DOI:
https://doi.org/10.3145/thinkepi.2024.e18a31Keywords:
Artificial Intelligence, Generative AI Chatbots, Information behaviour, Bibliographic search, Academic journals, Web analytics, Google Analytics, SEO, GEO, ASEOAbstract
This paper is a reflection on the role of generative AI chatbots in the referred web traffic that reaches academic journals. Scenarios on informational search behaviour within the academic environment are explored, regarding the influence of these new tools based on Large Language Models (LLM) and on the generation of direct responses resulting from an iterative process of conversation with an AI chatbot. By exploring web analytics data obtained with Google Analytics for the RACO portal, a first approach is presented to the low volume of visits that, to date, it seems that academic journals are receiving from AI chatbots. Hypotheses are formulated to explain this low volume of traffic, assuming that the use of these AI tools is important and growing in the academic world. Finally, the role of Generative Engine Optimization (GEO) as a strategy for academic content optimization in generative AI-powered search tools is discussed, to decide whether it should be a suitable complement to the already established Academic Search Engine Optimization (ASEO) actions. Discussion is invited through a series of open questions on how new AI tools may be changing the landscape of bibliographic information search and use, and how that may affect scholarly journals in their visibility and optimization strategies for traffic acquisition.References
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